Deep convolutional neural networks for accurate somatic mutation detection.
Sayed Mohammad Ebrahim Sahraeian,Ruolin Liu,Bayo Lau,Karl Podesta,Marghoob Mohiyuddin,Hugo Y. K. Lam +5 more
Reads0
Chats0
TLDR
NeuSomatic is presented, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities.Abstract:
Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a stand-alone somatic mutation detection method or with an ensemble of existing methods to achieve the highest accuracy.read more
Citations
More filters
Journal ArticleDOI
Computational network biology: Data, models, and applications
Chuang Liu,Yifang Ma,Jing Zhao,Ruth Nussinov,Ruth Nussinov,Yi-Cheng Zhang,Yi-Cheng Zhang,Feixiong Cheng,Feixiong Cheng,Feixiong Cheng,Zi-Ke Zhang,Zi-Ke Zhang +11 more
TL;DR: This review summarizes the recent developments of computational network biology, first introducing various types of biological networks and network structural properties, and then reviewing the network-based approaches, ranging from some network metrics to the complicated machine-learning methods.
Journal ArticleDOI
Identification of neoantigens for individualized therapeutic cancer vaccines
TL;DR: In this paper , a new classification of neoantigens, distinguishing between guarding, restrained and ignored, is presented, based on how they confer proficient antitumour immunity in a given clinical context.
Journal ArticleDOI
Training confounder-free deep learning models for medical applications.
TL;DR: This article introduces an end-to-end approach for deriving features invariant to confounding factors while accounting for intrinsic correlations between the confounder(s) and prediction outcome, exploiting concepts from traditional statistical methods and recent fair machine learning schemes.
Journal ArticleDOI
Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process.
Wei Feng,Nicholas Van Halm-Lutterodt,Hao Tang,Andrew Mecum,Mohamed Kamal Mesregah,Yuan Ma,Haibin Li,Feng Zhang,Zhiyuan Wu,Erlin Yao,Xiuhua Guo +10 more
TL;DR: 3D-CNN-SVM proves to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners, indicating that it can potentially be exploited by untrained operators and extended to virtual patient imaging data.
Journal ArticleDOI
Curated variation benchmarks for challenging medically relevant autosomal genes
TL;DR: In this paper , the Genome in a Bottle Consortium has provided variant benchmark sets, but these exclude nearly 400 medically relevant genes due to their repetitiveness or polymorphic complexity, which poses a challenge for their accurate analysis in a clinical setting.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.
Posted ContentDOI
Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM
TL;DR: BWA-MEM automatically chooses between local and end-to-end alignments, supports paired-end reads and performs chimeric alignment, which is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases.
Journal ArticleDOI
Comprehensive molecular characterization of human colon and rectal cancer
Donna M. Muzny,Matthew N. Bainbridge,Kyle Chang,Huyen Dinh,Jennifer Drummond,Gerald R. Fowler,Christie Kovar,Lora Lewis,Margaret Morgan,Irene Newsham,Jeffrey G. Reid,Jireh Santibanez,Eve Shinbrot,Lisa R. Trevino,Yuan Qing Wu,Min Wang,Preethi H. Gunaratne,Preethi H. Gunaratne,Lawrence A. Donehower,Chad J. Creighton,David A. Wheeler,Richard A. Gibbs,Michael S. Lawrence,Douglas Voet,Rui Jing,Kristian Cibulskis,Andrey Sivachenko,Andrey Sivachenko,Petar Stojanov,Aaron McKenna,Eric S. Lander,Eric S. Lander,Stacey Gabriel,Li Ding,Robert S. Fulton,Daniel C. Koboldt,Todd Wylie,Jason Walker,David J. Dooling,Lucinda Fulton,Kim D. Delehaunty,Catrina Fronick,Ryan Demeter,Elaine R. Mardis,Richard K. Wilson,Andy Chu,Hye Jung E. Chun,Andrew J. Mungall,Erin Pleasance,A. Gordon Robertson,Dominik Stoll,Miruna Balasundaram,Inanc Birol,Yaron S.N. Butterfield,Eric Chuah,Robin J.N. Coope,Noreen Dhalla,Ranabir Guin,Carrie Hirst,Martin Hirst,Robert A. Holt,Darlene Lee,Haiyan I. Li,Michael Mayo,Richard A. Moore,Jacqueline E. Schein,Jared R. Slobodan,Angela Tam,Nina Thiessen,Richard Varhol,Thomas Zeng,Yongjun Zhao,Steven J.M. Jones,Marco A. Marra,Adam J. Bass,Alex H. Ramos,Gordon Saksena,Andrew D. Cherniack,Stephen E. Schumacher,Barbara Tabak,Scott L. Carter,Nam Pho,Huy V. Nguyen,Robert C. Onofrio,Andrew Crenshaw,Kristin G. Ardlie,Rameen Beroukhim,Wendy Winckler,Matthew Meyerson,Alexei Protopopov,Angela Hadjipanayis,Eunjung Lee,Ruibin Xi,Lixing Yang,Xiaojia Ren,Narayanan Sathiamoorthy,Peng Chieh Chen,Psalm Haseley,Yonghong Xiao,Semin Lee,Jonathan G. Seidman,Lynda Chin,Peter J. Park,Raju Kucherlapati,J. Todd Auman,Katherine A. Hoadley,Ying Du,Matthew D. Wilkerson,Yan Shi,Christina Liquori,Shaowu Meng,Ling Li,Yidi J. Turman,Michael D. Topal,Donghui Tan,Scot Waring,Elizabeth Buda,Jesse Walsh,Corbin D. Jones,Piotr A. Mieczkowski,Darshan Singh,Junyuan Wu,Anisha Gulabani,Peter Dolina,Tom Bodenheimer,Alan P. Hoyle,Janae V. Simons,Matthew G. Soloway,Lisle E. Mose,Stuart R. Jefferys,Saianand Balu,Brian O'Connor,Jan F. Prins,Derek Y. Chiang,D. Neil Hayes,Charles M. Perou,Toshinori Hinoue,Daniel J. Weisenberger,Dennis T. Maglinte,Fei Pan,Benjamin P. Berman,David Van Den Berg,Hui Shen,Timothy J. Triche,Stephen B. Baylin,Peter W. Laird,Gad Getz,Michael S. Noble,Doug Voat,Nils Gehlenborg,Daniel DiCara,Juinhua Zhang,Hailei Zhang,Chang-Jiun Wu,Spring Yingchun Liu,Sachet A. Shukla,Lihua Zhou,Pei Lin,Richard W. Park,Marc Danie Nazaire,James A. Robinson,Helga Thorvaldsdottir,Jill P. Mesirov,Vesteinn Thorsson,Sheila Reynolds,Brady Bernard,Richard Kreisberg,Jake Lin,Lisa Iype,Ryan Bressler,Timo Erkkilä,Madhumati Gundapuneni,Yuexin Liu,Adam Norberg,Thomas Robinson,Da Yang,Wei Zhang,Ilya Shmulevich,Jorma J. de Ronde,Jorma J. de Ronde,Nikolaus Schultz,Ethan Cerami,Giovanni Ciriello,Arthur P. Goldberg,Benjamin Gross,Anders Jacobsen,Jianjiong Gao,Bogumil Kaczkowski,Rileen Sinha,B. Arman Aksoy,Yevgeniy Antipin,Boris Reva,Ronglai Shen,Barry S. Taylor,Marc Ladanyi,Chris Sander,Rehan Akbani,Nianxiang Zhang,Bradley M. Broom,Tod D. Casasent,Anna K. Unruh,Chris Wakefield,Stanley R. Hamilton,R. Craig Cason,Keith A. Baggerly,John N. Weinstein,David Haussler,Christopher C. Benz,Joshua M. Stuart,Stephen C. Benz,J. Zachary Sanborn,Charles J. Vaske,Jingchun Zhu,Christopher Szeto,Gary K. Scott,Christina Yau,Sam Ng,Theodore C. Goldstein,Kyle Ellrott,Eric A. Collisson,Aaron E. Cozen,Daniel R. Zerbino,Christopher Wilks,Brian Craft,Paul T. Spellman,Robert Penny,Troy Shelton,Martha Hatfield,Scott Morris,Peggy Yena,Candace Shelton,Mark Sherman,Joseph Paulauskis,Julie M. Gastier-Foster,Julie M. Gastier-Foster,Jay Bowen,Nilsa C. Ramirez,Nilsa C. Ramirez,Aaron D. Black,Robert E. Pyatt,Robert E. Pyatt,Lisa Wise,Peter White,Peter White,Monica M. Bertagnolli,Jen Brown,Timothy A. Chan,Gerald C. Chu,Christine Czerwinski,Fred Denstman,Rajiv Dhir,Arnulf Dörner,Charles S. Fuchs,Jose G. Guillem,Mary Iacocca,Hartmut Juhl,Andrew Kaufman,Bernard Kohl,Xuan Van Le,Maria C. Mariano,Elizabeth N. Medina,Michael Meyers,Garrett M. Nash,P. Paty,Nicholas J. Petrelli,Brenda Rabeno,William G. Richards,David B. Solit,Pat Swanson,Larissa K. Temple,Joel E. Tepper,Richard Thorp,Efsevia Vakiani,Martin R. Weiser,Joseph Willis,Gary Witkin,Zhaoshi Zeng,Michael J. Zinner,Carsten Zornig,Mark A. Jensen,Robert Sfeir,Ari B. Kahn,Anna L. Chu,Prachi Kothiyal,Zhining Wang,Eric E. Snyder,Joan Pontius,Todd Pihl,Brenda Ayala,Mark Backus,Jessica Walton,Jon Whitmore,Julien Baboud,Dominique L. Berton,Matthew C. Nicholls,Deepak Srinivasan,Rohini Raman,Stanley Girshik,Peter A. Kigonya,Shelley Alonso,Rashmi N. Sanbhadti,Sean P. Barletta,John M. Greene,David Pot,Kenna R. Mills Shaw,Laura A.L. Dillon,Kenneth H. Buetow,Tanja Davidsen,John A. Demchok,Greg Eley,Martin L. Ferguson,Peter Fielding,Carl F. Schaefer,Margi Sheth,Liming Yang,Mark S. Guyer,Bradley A. Ozenberger,Jacqueline D. Palchik,Jane Peterson,Heidi J. Sofia,Elizabeth J. Thomson +320 more
TL;DR: Integrative analyses suggest new markers for aggressive colorectal carcinoma and an important role for MYC-directed transcriptional activation and repression.
Journal ArticleDOI
dbSNP: the NCBI database of genetic variation
Stephen T. Sherry,Minghong Ward,Michael Kholodov,Jonathan Baker,Lon Phan,Elizabeth M. Smigielski,Karl Sirotkin +6 more
TL;DR: The dbSNP database is a general catalog of genome variation to address the large-scale sampling designs required by association studies, gene mapping and evolutionary biology, and is integrated with other sources of information at NCBI such as GenBank, PubMed, LocusLink and the Human Genome Project data.